Important Facts about Machine Learning in Healthcare

Machine learning is a branch of artificial intelligence that relies upon a machine or system being able to learn from incoming data rather than having to be programmed.  It is an evolutionary step that is only in its infancy and has improved production and eliminated waste in many businesses and industries.  The healthcare industry is slightly behind most everyone else in this factor due to the vast amount of data that is collected and the different types of complex data that is unique to the trade.  This gap is closing all the time with technological advances that are able to process this unique information.  Thus, there are many important facts about machine learning in healthcare that are worth a serious look.

 

Fact 1 – machine learning helps to save lives

 

The human body has the propensity to react the same to illnesses, diseases and injuries.  These facts and statistics are always being tried, tested and proved throughout the world.  That information is being collected every time someone steps into a doctor’s office or uses an emergency department.  From that data input, doctors are able to prescribe the right medicines, recommend the best treatments and diagnose ailments with the assurance that they are following best practice standards across the board.  Although this sounds very simplistic, a doctor doesn’t have to be a specialist in every area, but is able to pull up information related to a specific patient, having the backing of verifiable facts, and real-time analysis to work with.  All of this together helps to do things like compare patients with similar symptoms and define the best way to help that patient, or if he or she might be susceptible to other health issues advise further testing. 

 

Many healthcare organizations are turning to more healthcare input from things like smartphones, wearable technology and DNA analysis.  And for this exact reason of managing an almost constant influx of data is it imperative that the industry employ machine learning; it is humanly impossible to begin to do deep learning of the information and come out with a timely and accurate method of treatment.  Machine systems are beginning to tackle the diverse types of healthcare data, correlate patterns that exist, apply individual’s needs and uniquenesses and continually grow in depth and breadth of knowledge.  This changes the outlook for the future of healthcare and the ability to identify problems more quickly and accurately, along with providing the best care possible at the right time.

 

Fact 2 – machine learning helps to predict

There are many factors that make up healthcare; everything from one-on-one treatment, to insurance payments, to follow-up care.  Machine learning systems take into account prior history of a patient, geographic and economic influences, and can make predictions on patients that might have issues when it comes to not taking their medications, not paying their bills, not being able to make their next appointment or might end up back in the hospital.  The reason that this kind of information is so important is that elements like these add to the cost of care, whether up front and immediate, or in the long-term with extended medical care requirements. 

 

When a doctor or medical professional is able to identify people that need more help, it is much easier to come up with solutions that can make an impact rather than playing a game of catch-them-if-you-can.  For example: if there is a patient that lives on Social Security, has no reliable means of transportation and has several medications for several different health concerns, this is a person that will probably have a hard time paying for care, may miss appointments and might end up needing an ambulance ride to the emergency department when complications set in. 

 

If healthcare professionals are able to sit down and discuss each impedance, and possible solutions to them, there is a real possibility of helping this patient to avoid costly setbacks financially and physically.  Some organizations are able to do teleconferencing instead of coming in for an office visit.  Others provide representatives that can make home visits or can mail prescriptions to a home.  Even making follow-up calls to ensure the patient understands health guidelines that he or she should be sticking to can help prevent problematic situations in the future.

 

Fact 3 – machine learning help things run more efficiently

Just like with the human body, there are patterns to the way an organization runs.  Knowing these patterns can help to isolate where and when wasteful time and resources are being spent, or ways in which restructuring schedules or layout could be beneficial.  Machine learning takes into account current and past practices within the organization and analyzes them for inefficiencies.  Goals set within the business also play a large factor in how expectations could be set.  What this all adds up to is utilizing the data from the machine learning system to determine how best to run the organization. 

 

In the past, this has been done by software specifically designed for the healthcare industry, however, not all of the information produced by the software is timely, helpful or completely accurate.  The application of machine learning helps to move information more quickly, to drill down deeper and uncover patterns or models that are or aren’t working, to provide insight that may never have been available before, and to do so much more efficiently.  This sort of architype not only benefits the inner workings of an organization, but also the efficiency and effectiveness of care given to patients.

 

Fact 4 – machine learning has only just broken surface of possibility

With many technological advances, what the current picture and usage are may be very different to what the short- and long-term evolutions become.  However, you have to start with these steps to discover the next.  Many in the healthcare industry hope to see various tasks, both mundane and intricate, moved to the capable hands of artificial intelligence and machine learning.  When delicate surgery is performed by a robot with little or no assistance from a physician, or when routine check-ups are handled without any intervention, many healthcare professionals will believe that a true movement in the industry has happened.  Yet, when comparing old TV shows that tried to grasp what might be possible, some things they got right, while other things they completely missed.  We may not even be able to image what is possible until we’ve traveled further down the road of possibilities.

 

Truly, there are many other important facts about machine learning in healthcare, along with the great possibilities that have yet to be realized.  But what we do know is that there are real-life applications being utilized with healthcare and machine learning.  We are benefiting greatly from all the knowledge being gained, and we are seeing this translated into better, more accurate care, and more efficiency in the system as a whole.  As time goes on, we will probably see more application of machine learning in healthcare as well as many changes to the industry as it evolves to apply it more to everyday medical events.

 

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